The primary purpose of this study is to determine the impact of prenatal psychiatric disorders on birth outcomes, health service utilization and costs. Infant mortality and morbidity rates in this country continue to exceed those of most industrialized countries. For low-income women, particularly African Americans and those with limited access to health services, infant mortality and morbidity rates approximate those of third-world countries. Although biological, genetic and psychosocial factors are linked to poor birth outcomes, one factor that has been overlooked is prenatal psychiatric disorders, despite compelling evidence associating mental health problems with maternal-infant health. Even less is known about how our health service delivery system contributes to poor birth outcomes through inadequate detection and treatment of prenatal psychiatric disorders. A better understanding of how psychiatric illness influences the prenatal and postpartum use of health services and costs is also needed. This study will use a prospective cohort research design to compare outcomes between low- income pregnant women with and without psychiatric disorders who are stratified by race and urban-rural residence. Using the Composite International Diagnostic Interview (CIDI), the presence or absence of current psychiatric disorders will first be determined in a sample of 1095 pregnant Medicaid recipients who receive services in Special Supplemental Nutrition Programs for Women, Infants and Children (WIC). A sample of 219 pregnant women with current psychiatric diagnoses and a comparison group of 211 pregnant women without psychiatric diagnoses will be followed for three months postpartum to ascertain birth outcomes, use of health services and cost of these services. Using Birth certificate and Medicaid claims data, multiple or logistic regression will determine the independent effect of current psychiatric disorders on birth outcomes and health service utilization rates, controlling for other explanatory variables. Differences in cost estimates from diagnosis related cost weights and total charges will be explained using multiple regression analysis.